Intro

Background Information: We were tasked by Kirk Bogard, the Associate Vice President for Development and External Relations at Miami University to explore a dataset of real student data in order to find relationships and patterns that he can use to give Miami a competitive advantage. After exploring the data, we found a few particular variables that can help us find a potential relationship in the dataset, survey_salary, survery_internships, and survey_state. We plan to build a regression model using the number of internships during college to predict salary after graduation, using state to control for salary. The purpose of this analysis is to provide information on the relationship between number of internships and salary information to FSB Career Services. This will help them give more accurate guidance to students to ensure they get the best full time opportunity for them.

Survey Overview:

Internship Effects on Salary:

Using a regression model we predicted salary by number of internships using 0 internships as a baseline.

• Having 1 internship increases the predicted salary by $5243 compared to 0 internships

• Having 2 internships increases the predicted salary by $7775 compared to 0 internships

• Having 3 internships increases the predicted salary by $9873 compared to 0 internships

• Having 4 internships increases the predicted salary by $9894 compared to 0 internships

• Having 5 internships increases the predicted salary by $9388 compared to 0 internships

There is a sizable increase in salary when adding another internship up until 3 internships, 4 internships is about the same as 3 internships and 5 internships even drops the predicted salary.

External Research:

Survey Overview

Overview of survey responses

rate = round(100 * nrow(df)/nrow(df2),0)

gauge(rate, min=0, max=100, symbol='%', gaugeSectors(
  success=c(80,100), warning= c(40,79), danger=c(0,39)
)
      )

Internship Effects on Salary

Call:
lm(formula = df$survey_salary ~ df$survey_internships)

Residuals:
   Min     1Q Median     3Q    Max 
-51384  -6755    245   5745 115713 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)             51511.7      970.6  53.071  < 2e-16 ***
df$survey_internships1   5243.0     1086.3   4.826 1.51e-06 ***
df$survey_internships2   7775.3     1065.8   7.296 4.52e-13 ***
df$survey_internships3   9872.5     1220.7   8.088 1.14e-15 ***
df$survey_internships4   9893.8     2207.3   4.482 7.87e-06 ***
df$survey_internships5   9388.3     4258.6   2.205   0.0276 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 11730 on 1722 degrees of freedom
Multiple R-squared:  0.04643,   Adjusted R-squared:  0.04366 
F-statistic: 16.77 on 5 and 1722 DF,  p-value: 3.286e-16